fix: next scheduled date time response issue

.devcontainer/devcontainer.json

@@ -32,9 +32,9 @@
 			]
 		}
 	},
-	"postCreateCommand": "bash ./.devcontainer/setup_env.sh",
+	"postCreateCommand": "sed -i 's/\\r$//' ./.devcontainer/setup_env.sh && find ./infra -type f -name \"*.sh\" -exec sed -i 's/\\r$//' {} \\; && bash ./.devcontainer/setup_env.sh",
 	"remoteUser": "vscode",
 	"hostRequirements": {
 		"memory": "4gb"
 	}
-}
+}

.github/workflows/CAdeploy.yml

@@ -7,6 +7,10 @@ on:
   schedule:
     - cron: '0 6,18 * * *'  # Runs at 6:00 AM and 6:00 PM GMT
 
+env:
+  GPT_MIN_CAPACITY: 10
+  TEXT_EMBEDDING_MIN_CAPACITY: 10
+
 jobs:
   deploy:
     runs-on: ubuntu-latest
@@ -22,8 +26,8 @@ jobs:
           export AZURE_TENANT_ID=${{ secrets.AZURE_TENANT_ID }}
           export AZURE_CLIENT_SECRET=${{ secrets.AZURE_CLIENT_SECRET }}
           export AZURE_SUBSCRIPTION_ID="${{ secrets.AZURE_SUBSCRIPTION_ID }}"
-          export GPT_MIN_CAPACITY="11"
-          export TEXT_EMBEDDING_MIN_CAPACITY="45"
+          export GPT_MIN_CAPACITY=${{ env.GPT_MIN_CAPACITY }}
+          export TEXT_EMBEDDING_MIN_CAPACITY=${{ env.TEXT_EMBEDDING_MIN_CAPACITY }}
           export AZURE_REGIONS="${{ vars.AZURE_REGIONS_CA }}"
 
           chmod +x infra/scripts/checkquota.sh
@@ -77,7 +81,7 @@ jobs:
         id: generate_rg_name
         run: |
           echo "Generating a unique resource group name..."
-          ACCL_NAME="cabyoc"  # Account name as specified
+          ACCL_NAME="ca"  # Account name as specified
           SHORT_UUID=$(uuidgen | cut -d'-' -f1)
           UNIQUE_RG_NAME="arg-${ACCL_NAME}-${SHORT_UUID}"
           echo "RESOURCE_GROUP_NAME=${UNIQUE_RG_NAME}" >> $GITHUB_ENV
@@ -115,18 +119,26 @@ jobs:
           az deployment group create \
             --resource-group ${{ env.RESOURCE_GROUP_NAME }} \
             --template-file infra/main.bicep \
-            --parameters AzureOpenAILocation=${{ env.AZURE_LOCATION }} environmentName=${{ env.SOLUTION_PREFIX }} cosmosLocation=eastus2
+            --parameters AzureOpenAILocation=${{ env.AZURE_LOCATION }} environmentName=${{ env.SOLUTION_PREFIX }} cosmosLocation=eastus2 gptDeploymentCapacity=${{ env.GPT_MIN_CAPACITY }} embeddingDeploymentCapacity=${{ env.TEXT_EMBEDDING_MIN_CAPACITY }} \
 
       - name: Get AI Services name and store in variable
+        if: always() && steps.check_create_rg.outcome == 'success'
         id: get_ai_services_name
         run: |
           set -e
           echo "Getting AI Services name..."
-          # Get the AI Services name from the deployment output
+          # Get the AI Services name
           ai_services_name=$(az cognitiveservices account list -g ${{ env.RESOURCE_GROUP_NAME }} --query "[0].name" -o tsv)
-          echo "AI_SERVICES_NAME=${ai_services_name}" >> $GITHUB_ENV
+          if [ -z "$ai_services_name" ]; then
+            echo "No AI Services resource found in the resource group."
+            echo "AI_SERVICES_NAME=" >> $GITHUB_ENV
+          else
+            echo "AI_SERVICES_NAME=${ai_services_name}" >> $GITHUB_ENV
+            echo "Found AI Services resource: $ai_services_name"
+          fi
 
       - name: List KeyVaults and Store in Array
+        if: always() && steps.check_create_rg.outcome == 'success'
         id: list_keyvaults
         run: |
 
@@ -180,7 +192,7 @@ jobs:
       #     echo "Power BI URL updated successfully for application: $application_name."
 
       - name: Delete Bicep Deployment
-        if: success()
+        if: always()
         run: |
           set -e  
           echo "Checking if resource group exists..."
@@ -197,6 +209,7 @@ jobs:
           fi
 
       - name: Wait for resource deletion to complete
+        if: always() && steps.check_create_rg.outcome == 'success'
         run: |
 
           # List of keyvaults 
@@ -256,7 +269,7 @@ jobs:
           done
 
       - name: Purging the Resources
-        if: success()
+        if: always() && steps.check_create_rg.outcome == 'success'
         run: |
 
           set -e 

TRANSPARENCY_FAQ.md

@@ -7,7 +7,7 @@
   The sample solution for research assistant focuses on a researcher who wants to explore relevant articles and grants to accelerate submission of a grant proposal. The sample data is sourced from a select set of research and grants published on PubMed and NIH. The documents are intended for use as sample data only. The sample solution takes user input in text format and returns LLM responses in text format up to 800 tokens.
   It uses prompt flow to search data from AI search vector store, summarize the retrieved documents with Azure OpenAI.
 
-  The sample solution for client advisor focuses on an advisor to save time and prepare relevant discussion topics for scheduled meetings. It provides an overview of daily client meetings with seamless navigation between viewing client profiles and chatting with data. The sample data is synthetic and is generated using ficticious names. This data intended for use as sample data only. The sample solution takes user input in text format and returns LLM responses in text format up to 800 tokens.
+  The sample solution for client advisor focuses on an advisor to save time and prepare relevant discussion topics for scheduled meetings. It provides an overview of daily client meetings with seamless navigation between viewing client profiles and chatting with data. The sample data is synthetic and is generated using fictitious names. This data intended for use as sample data only. The sample solution takes user input in text format and returns LLM responses in text format up to 800 tokens.
   It uses semantic kernel to search data from AI search vector store and Azure SQL Database, summarize the results using Azure OpenAI.
 
 - ### What is/are Build your own copilot Solution Accelerator’s intended use(s)?  

docs/DeploymentGuide.md

@@ -10,25 +10,23 @@ Check the [Azure Products by Region](https://azure.microsoft.com/en-us/explore/g
 - [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/) 
 - [Azure App Service](https://learn.microsoft.com/en-us/azure/app-service/)
 - [Azure SQL Database](https://learn.microsoft.com/en-us/azure/azure-sql/)
-- [Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/)
 - [Azure Semantic Search](AzureSemanticSearchRegion.md)  
 
 Here are some example regions where the services are available: East US, East US2, Australia East, UK South, France Central.
 
 
 ### **Important: Check Azure OpenAI Quota Availability**
 
-⚠️ To ensure sufficient quota is available in your subscription, please follow [quota check instructions guide](./quota_check.md) before you deploy the solution.
+⚠️ To ensure sufficient quota is available in your subscription, please follow [quota check instructions guide](./QuotaCheck.md) before you deploy the solution.
 
 
 ### [Optional] Quota Recommendations  
-By default, the **Gpt-4o-mini model capacity** in deployment is set to **30k tokens**, so we recommend
+By default, the **Gpt-4o-mini model capacity** in deployment is set to **30k tokens**, so we recommend updating the following:
 
 > **For Global Standard | GPT-4o-mini - increase the capacity to at least 150k tokens post-deployment for optimal performance.**
 
-To adjust quota settings, follow these [steps](AzureGPTQuotaSettings.md)
-
-
+Depending on your subscription quota and capacity, you can [adjust quota settings](AzureGPTQuotaSettings.md) to better meet your specific needs. You can also [adjust the deployment parameters](CustomizingAzdParameters.md) for additional optimization.
+­
 ## Deployment Options & Steps
 
 Pick from the options below to see step-by-step instructions for GitHub Codespaces, VS Code Dev Containers, and Local Environments.
@@ -211,10 +209,6 @@ This will rebuild the source code, package it into a container, and push it to t
 2. **Add Authentication Provider**  
     - Follow steps in [App Authentication](./AppAuthentication.md) to configure authenitcation in app service. Note that Authentication changes can take up to 10 minutes. 
 
-3. **Fabric Configuration**, 
-   - Follow steps in [Fabric Deployment guide](FabricDeployment.md) to set up the data processing pipelines and Power BI report in Fabric.
-4. **Teams App Configuration**
-   - *(Optional)* Follow steps in [Teams Tab App guide](TeamsAppDeployment.md) to add the Client Advisor app to Microsoft Teams.
-5. **Deleting Resources After a Failed Deployment**  
+3. **Deleting Resources After a Failed Deployment**  
 
      - Follow steps in [Delete Resource Group](DeleteResourceGroup.md) if your deployment fails and/or you need to clean up the resources.

docs/FabricDeployment.md

@@ -12,7 +12,7 @@
 ## Step 2: Deploy Fabric resources and artifacts
 1.   Navigate to ([Azure Portal](https://portal.azure.com/))
 2.   Click on Azure Cloud Shell in the top right of navigation Menu (add image)
-3.   Run the run the following commands:  
+3.   Run the following commands:  
       - ```az login``` 
       - ```rm -rf Build-your-own-copilot-Solution-Accelerator```
       - ```git clone https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator```

docs/quota_check.md → docs/QuotaCheck.md

@@ -55,7 +55,7 @@ eastus, uksouth, eastus2, northcentralus, swedencentral, westus, westus2, southc
 ### **Sample Output**
 The final table lists regions with available quota. You can select any of these regions for deployment.
 
-![quota-check-ouput](images/readMe/quota-check-output.png)
+![quota-check-ouput](images/readme/quota-check-output.png)
 
 ---
 ### **If using Azure Portal and Cloud Shell**

docs/SampleQuestions.md

@@ -5,8 +5,8 @@ To help you get started, here are some **Sample Prompts** you can ask after sele
 1. Click on **Karen Berg** client.
 2. Ask the following questions:
    - What were Karen's concerns during our last meeting?
-   - Did karen express any concerns over market fluctuations in prior meetings?
-   - What type of asset does karen own ?
+   - Did Karen express any concerns over market fluctuations in prior meetings?
+   - What type of asset does Karen own ?
    - Show latest asset value by asset type?
    - How did equities asset value change in the last six months?